Symmetry based air pocket detection methods and systems

ABSTRACT

Methods and systems for use in detecting an air pocket in a single crystal material are described. One example method includes providing a matrix including a plurality of data units, the plurality of data units including image data related to a region of interest of the single crystal material; defining a first half and a second half of the matrix based on a first axis passing through the center of the matrix; determining, by a processor, a difference between each data unit of the first half and a corresponding data unit of the second half; calculating, by the processor, a first index value based on the determined differences; and identifying an air pocket within the single crystal material based on the first index value and a predetermined threshold.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Application No.61/580,900 filed Dec. 28, 2011, the entire disclosure of which is herebyincorporated by reference in its entirety.

FIELD

This disclosure relates generally to air pocket detection systems andmethods and, more specifically, to detection of one or more air pocketsin a single crystal material based on the symmetry of an image objectwithin a region of interest of the material.

BACKGROUND

Single crystal ingots, such as silicon ingots, are grown and processedinto semiconductor wafers. During processing, one or more tests orinspections may be performed to determine if one or more air pockets(e.g., voids) exist within the ingot, before and/or after slicing intowafers. Detection of the air pocket early in the processing is desiredto avoid further processing of portions of the ingot having the airpocket, because the air pocket may affect the structural integrity ofthe ingot and/or usefulness of the ingot in one or more products.Detection of air pockets prior to shipment of product wafers may berequired to prevent failure of the wafer at some future processing, suchas during manufacturing of a semiconductor and/or photovoltaic device.

This section is intended to introduce the reader to various aspects ofart that may be related to various aspects of the disclosure, which aredescribed and/or claimed below. This discussion is believed to behelpful in providing the reader with background information tofacilitate a better understanding of the various aspects of the presentdisclosure. Accordingly, it should be understood that these statementsare to be read in this light, and not as admissions of prior art.

SUMMARY

In one aspect of the present disclosure, a computer-implemented methodfor use in detecting an air pocket in a single crystal material isprovided. The method includes providing a matrix including a pluralityof data units, the plurality of data units including image data relatedto a region of interest of the single crystal material; defining a firsthalf and a second half of the matrix based on a first axis passingthrough the center of the matrix; determining, by a processor, adifference between each data unit of the first half and a correspondingdata unit of the second half; calculating, by the processor, a firstindex value based on the determined differences; and identifying an airpocket within the single crystal material based on the first index valueand a predetermined threshold.

Another aspect of the present disclosure is a detection system for usein detecting an air pocket in a material. The detection system includesa light source configured to emit near-infrared (NIR) light toward amaterial, a detection device positioned adjacent to the material tocapture image data based on light passing through the material, and aprocessor coupled to the light source and the detection device. Theprocessor is configured to provide a matrix including a plurality ofdata units and a center, define a first half and a second half of thematrix based on a first axis passing through the center of the matrix,determine a difference between each data unit of the first half and acorresponding data unit of the second half, calculate a first indexvalue based on the determined differences, and identify an air pocketwithin the single crystal material based on the first index value and apredetermined threshold.

Yet another aspect of the present disclosure is one or morenon-transitory computer-readable storage media havingcomputer-executable instructions embodied thereon. When executed by atleast one processor the computer-executable instructions cause the atleast one processor to identify a matrix including a plurality of dataunits and a center, define a first half and a second half of the matrixbased on a first axis passing through the center of the matrix,determine a difference between each data unit of the first half and acorresponding data unit of the second half, calculate a first indexvalue based on the determined differences, and identify an air pocketwithin the single crystal material based on the first index value and apredetermined threshold. The plurality of data units includes image datarelated to a region of interest of the single crystal material.

Various refinements exist of the features noted in relation to theabove-mentioned aspects of the present disclosure. Further features mayalso be incorporated in the above-mentioned aspects of the presentdisclosure as well. These refinements and additional features may existindividually or in any combination. For instance, various featuresdiscussed below in relation to any of the illustrated embodiments of thepresent disclosure may be incorporated into any of the above-describedaspects of the present disclosure, alone or in any combination.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an exemplary detection system.

FIGS. 2A, 2B, and 2C illustrate image data for a material having an airpocket (APK) anomaly.

FIGS. 3A, 3B, and 3C illustrate image data for a material having anon-APK anomaly.

FIGS. 4A, 4B, and 4C illustrate image data for a material having an APKanomaly, with a mask applied to the matrix.

FIG. 5 is a block diagram illustrating four exemplary axes of thematerial of FIG. 2.

Corresponding reference characters indicate corresponding partsthroughout the drawings.

DETAILED DESCRIPTION

The systems described herein are operable to detect light passingthrough a single crystal material, such as a single crystal sample, andprocess the image data based on the detected light to determine if anair pocket (APK) is present within the material. Generally, APKanomalies (e.g., voids) define substantially circular shapes, whilenon-APK anomalies deviate from a circular shape. As such, the systemsdescribed herein determine the symmetry of the intensity of image datafor a material to discriminate between APK and non-APK anomalies in thematerial.

In one embodiment, technical effects of the methods, systems, andcomputer-readable media described herein include at least one of: (a)providing a matrix including a plurality of data units, the plurality ofdata units including image data related to a region of interest of thesingle crystal material, (b) determining, by a processor, a differencebetween data units of the matrix and corresponding data units of thematrix, wherein the corresponding data units are defined by a firstoperation on the matrix, (c) calculating, by the processor, a firstindex value based on the differences of the corresponding data units,and (d) identifying an air pocket within the single crystal materialbased on the first index value and a predetermined threshold.

Referring to the drawings, an exemplary detection system is shown inFIG. 1 and indicated generally at 100. In this embodiment, detectionsystem 100 includes a light source 102, such as a near-IR ornear-infrared light source, to direct light toward a material 104. Thelight source 102 is configured to provide light, which defines awavelength sufficient to penetrate the material 104. In variousembodiments, the wavelength of the light from light source 102 (e.g.,near-infrared (NIR) light) is selected based on the thickness of thematerial 104. In one example, the wavelength of the near-IR lightemitted from the light source 102 is about 1 to about 2 microns. It iscontemplated, however, that light having one or more differentwavelengths can be emitted from light source 102.

On the opposite side of the material 104, the detection system 100includes a capture device 106 configured to capture the light passingthrough the material 104. In this example, the image capture device 106is a camera, such as a silicon-based CCD or CMOS array camera. Inanother example, the capture device 106 includes an InGaAs MOS arraycamera. Image arrays contemplated herein are two dimensional. Further,one dimensional line-scan or two dimensional time-delay integration (TDIline-scan) cameras with mechanized scanning may be used to create thetwo dimensional image arrays, while standard two dimensional array “snapshot” cameras may also be used. Single capture devices 104 may also beemployed, which are used to create two dimensional images using a Nipkowdisk or other method to scan an image across a single capture devices orseries of discrete capture devices. More generally, a variety ofdifferent types of capture devices 106 configured to capture light atthe particular wavelength emitted by the light source 102 andtransmitted through the material 104 are possible for this application.The capture device 106 is provided to generate two dimensional imagedata, which is substantially in-focus and representative of lightpassing through the material 104. The image data may be provided in asingle image or multiple images. Multiple images can be provided asmultiple image slices of the material 104, at different depths of thematerial 104, or from different perspectives, such as viewing orillumination angle.

The material 104 may include various different types of materials, suchas silicon, germanium, gallium arsenide, or other types of materialsformed through a crystalline process. In this embodiment, the singlecrystal material 104 is a Czochralski (CZ) grown material forming one ormore ingot sections, slices, wafers, slugs, slabs, and/or cylinders. Thematerial 104 shown in FIG. 1 is nominally plain parallel, such that thetop surface is generally parallel with the bottom surface of thematerial 104. In other examples, a material may be nearly plain parallelor non-plain parallel, such as cylindrical ingot sections.

In this embodiment, the single crystal material 104 may be subjected totesting at detection system 100 in a variety of conditions, including,for example, potentially doped with various dopants to some level, crude(such as slabs or slugs or after slicing, grinding, lapping or etching),polished (e.g., SSP wafer having front side only polished, back side invarious conditions or DSP wafer having both surfaces polished, withfront surface potentially final or kiss polished), and/or coated with anepitaxial layer of the same single crystal material except, potentially,a different doping level. Materials 104 may be provided in a variety ofthicknesses, such as, for example, from under 1 mm up to about 10's ofmm, or other thickness directly from a growing process or after one ormore processing steps.

Detection system 100 further includes a processor 108 and a memory 110coupled to the processor 108. Processor 108 may include one or moreprocessing units (e.g., a multi-core configuration). The term processor,as used herein, refers to central processing units, microprocessors,microcontrollers, reduced instruction set circuits (RISC), applicationspecific integrated circuits (ASIC), logic circuits, and/or any othercircuit or processor capable of executing instructions to performfunctions described herein. Further, processor 108 may include separatediscrete devices located proximate to one another, and/or remotely fromone another.

Memory 110 is one or more devices operable to enable information such asexecutable instructions and/or other data to be stored and/or retrieved.Memory 110 may include one or more computer readable media, such as,without limitation, hard disk storage, optical drive/disk storage,removable disk storage, flash memory, non-volatile memory, ROM, EEPROM,random access memory (RAM), etc. In several examples, memory 110includes one or more non-transitory computer-readable storage mediaconfigured to store, without limitation, computer-executableinstructions, image data, predetermined thresholds, and/or any othertypes of data referred to herein, expressly or inherently. Memory 110may be incorporated into and/or separate from processor 108, and/oraccessible through one or more networks (e.g., Cloud storage).

As used herein, the term “region of interest” may refer to any imageregion, including binary image or gray-scale image regions, thatincludes one or more image objects or blobs. As used herein, the term“image object” and “blob” may refer to, for example, data units of whichat least a portion are being evaluated by the methods and systemsdescribed herein. In some embodiments, the term “image object” may referto data units within a grey-scale image, while the term “blob” may referto data units within a binary image.

In use, the single crystal material 104 is positioned between the lightsource 102 and the capture device 106, such that light from the lightsource 102 is directed through the material 104, and captured by capturedevice 106, potentially requiring scanning of the material 104 or theimage capture device 106 to produce the captured two dimensional imagearray. The image data generated by the capture device 106 is provided toprocessor 108, which stores the image data in memory 110. Example imagedata of materials captured by capture device 106 is illustrated in FIGS.2A-4A. The systems and methods described herein are provided to processthe image data to determine if one or more APKs are present in thematerial 104.

In this embodiment, the image data is processed to provide a matrix ofdata units (e.g., picture elements or pixels) representative of a regionof interest (e.g., an anomaly) of the image data. In one example,processor 108 inverts the intensity of the image data according to oneor more thresholds, as shown in FIG. 2B-4B. The processing provides animage object of pixels (e.g., a blob), which are representative of ananomaly within the material 104. The processor 108 identifies theanomalies as regions of interest, such as region of interest 114 of FIG.2A, which may contain an APK. In at least one embodiment, processor 108may or may not apply one or more initial processes to identify larger,more easily identified APKs, while other methods herein may be used toidentify APKs and APK-like anomalies. For example, larger APKs oranomalies not resembling APKs may be identified during imaging of thematerial 104 by capture device 106, such as a line-scan or TDI line-scancamera. Further, in various embodiments, the processor 208 may initiallyperform one or more other operations on the image data, such asfiltering, inverting, or other operation to provide more efficientprocessing of the image data according to the methods provided herein.

Upon detection of a region of interest, the processor 108 defines amatrix of data units including at least one portion of the imageindicating the image object. In the illustrated examples, the processor108 provides the matrix 107 and 127 of gray-scale data units includingthe image objects 114 and 124, as shown in FIGS. 2B and 3B,respectively. In the examples, the matrixes 107 and 127 are squares. Inother embodiments, a matrix may include a different shape, which isgenerally symmetrical over at least one axis. The size of the matrix canpotentially be padded to include pixels outside the found image objector clipped to remove the outermost pixels of the image object prior tofurther processing. The threshold processing and/or other processingmethods may be further employed to improve the image quality of theregions of interest with respect to background noise. Further, a maskmay be applied to the image data based on a best-fit circle, best-fitellipse or threshold boundary positioned over the region of interest. Inthe example of FIGS. 4A-4C, a circle mask 111 is applied to the regionof interest 114 to limit noise and other affects outside of the mask111. In the example embodiment, the processor 108 sets the data units(e.g., pixels) outside the mask 111 to zero intensity or black. Itshould be appreciated that various different types and shapes of masks(e.g., ellipse, circle, image object threshold boundary) may be used ina variety of embodiments to enhance the efficiency and/or accuracy ofthe detection system 100. In other embodiments, such as shown in FIGS. 2and 3, a mask may be omitted or may include multiple weighting levels.

As shown in FIGS. 2A-2C, the matrix 107 is located on the image object114 within the image data. The image object 114 generally includes acenter 113, and processor 108 centers the matrix 107 substantially onthe center 113 of the image object 114. In one example, a matrix iscentered on the image object through use of bi-linear and/or bi-cubicinterpolation. More specifically, one or more interpolation algorithmsmay be applied to the existing data units within the matrix, eitherwithin the square material or prior to identifying the matrix, toenhance resolution of the image data. In one example, processor 108employs a polynomial interpolation based on existing data units to fillin additional data units into a matrix to provide enhanced resolution.In such an example, the processor 108 may expand an N×N pixel squarematrix, with N being an odd or even integer, into 4N×4N pixel squarematrix. Through use of interpolation, a center of the region of interestmay be more precisely located, and utilized as described below.Expansion of the matrix is commonly performed using bilinear orpreferably bi-cubic interpolation, or potentially other methods.

In one or more examples, the region of interest is centered based on a2D binary image blob-fit ellipse centroid, or center-fit of thegray-scale image data through use of an intensity-weighted center ofmass calculation, the binary image blob and gray-scale image objecthaving identical boundaries. Alternatively, the matrix may beun-centered on the image object, and/or include less than the entireimage object. In one or more embodiments, the matrix may be padded withadditional data units around the image object. For example, the matrixof FIG. 2B illustrates additional data units 115 surrounding the imageobject 114, while the matrix of FIG. 4B includes mask 111 to reduce thedata units surrounding the image object 114. Alternatively, the imageobject may be clipped, by the boundaries of the matrix, to include onlythe matrix data closest to the center of the image object.

The matrix includes any number of data units. In the example of FIG. 2B,the matrix 107 includes approximately 180×180 pixels of image data. Inthe example of FIG. 3B, the matrix 127 includes approximately 90×90pixels of image data. Each of the matrixes 107 and 127 is selected fromimage data having approximately 1000×750 pixels, as shown in FIGS. 2Aand 3A. Depending on several factors (e.g., the size of an imageobject), any number of data units may be included in the matrix. Amatrix may include from about 3×3 data units up to the entire capturedimage data, of the size that is provided from capture device 106.Furthermore, the matrix may be increased and/or decreased in size and/orresolution through one or more types of interpolation or sub-sampling.It should be understood that the size of the square matrix is avariable, which is selectable based on the amount of data to beprocessed by processor 108 within a time period suitable for inspectionat high volume or medium volume manufacturing in various embodiments.For example, when used in a fabrication process of material 104, theamount of data included in the matrix may be selected to providesufficient confidence, while permitting processing of the square imagein less than about 1.0 second or another suitable time interval.

When the matrix is identified, processor 108 defines multiple halves ofthe matrix, which are defined by multiple different axes of the matrix.Several example halves and example axes of matrix 107 are shown in FIG.5. Specifically, axis 200 extends vertically and defines a left half 208and a right half 210 of matrix 107, while axis 202 extends horizontallyand defines a top half and a bottom half. Further, axis 204 extends on adiagonal and defined a top-right half and a bottom-left half, while axis206 extends on a diagonal and defines top-left half and bottom-righthalf. It should be appreciated that the axes and halves illustrated inFIG. 5 are for purposes of illustration and do not limit the number oforientations of axes and/or halves. As used herein, the term “half”generally refers to a substantially equal dissection of the matrix,within a substantially equal number of data units included in each oftwo halves. Minor deviations from exactly half the number of data unitsshould be considered to be within the definition.

Further, as shown in this embodiment of FIG. 5, each of the axes 200,202, 204, and 206 pass through the center 109 of the matrix 107. In thisexample, the center 109 of the matrix 107 is substantially the same ascenter 113 of the image object 114. The center of the matrix may belocated otherwise, relative to the image object, in other embodiments.

With reference to vertical axis 200 of FIG. 5A, in this exemplaryembodiment, processor 108 performs a flip of the matrix 107 about axis200 and determines the difference between each data unit of the originalmatrix and a corresponding data unit of the flipped matrix. In anotherembodiment, processor 108 performs a fold of the matrix 107 about axis200 and determining the difference between each data unit of the lefthalf 208 and a corresponding data unit of the right half 210. Thecorresponding data unit includes a data unit on which a data unit wouldfall if the matrix 107 was physically folded and/or flipped onto itself,according to an axis, such as the axis 200. As shown in FIG. 5A, pixels212A and 212B represent one pair of corresponding data units. If theintensity of a region of interest included in the matrix defines asymmetrical shape, the difference between the corresponding data pointsshould be approximately zero. If, however, the region of interest isasymmetrical, several of the differences between corresponding dataunits will not be substantially zero.

The difference calculation used accumulates all differences with thesame sign, for example all differences are accumulated as positivevalues, so that any image difference adds to the magnitude of theaccumulation. For example, to achieve same sign differences, theaccumulation may be performed using the absolute value of per data unitdifferences or the square root of the per data unit differences squared.Other approaches to achieving the same sign of the per data unitdifferences or operations of the differences are also contemplated. Onesuch operation includes accumulating the squared differences as a sum ofcorresponding data units, dividing that sum by the number of differencessummed, then taking the square root of that quotient. This is referredto as the root mean square (or RMS) of the differences. It should beunderstood that other linear or non-linear weightings of differences arealso within the scope of the present disclosure.

Specifically, for example, as shown in FIG. 2A, the image object 114 oranomaly within the image data 118 is substantially circular.Accordingly, when the matrix 107 is folded over the vertical axis 200,the differences of each of the corresponding data units is shown in FIG.2C. Because the image object 114 is not perfectly circular with a cleantransition from inside to outside of the image object 114, somedifferences have a magnitude other than zero, as shown in FIG. 2C. Incontrast, as shown in the example of FIG. 3A, the image object 124within the image data 128 is substantially non-circular. Accordingly,when the matrix 127 is fold over its vertical axis, at least a portionof differences of each of the corresponding data units have magnitudessubstantially above zero, as shown in FIG. 3C. Because the image object124 (or anomaly) is non-circular and non-symmetric, the differences ofintensity are greater than the difference shown in FIG. 2C. In thismanner, to detect an APK based on the circular shape of the APK,processor 108 is able to determine the difference between correspondingdata units in halves of a matrix, as an indicator of the symmetry of theregion of interest. Note that due to symmetry the same-sign accumulationof differences can be performed across the entire matrix when the matrixis flipped, and can be performed across half of the matrix when thematrix is folded onto itself. In several examples, such accumulationsdiffer by a factor of two and/or nearly two.

Further, processor 108 calculates an index value based on theaccumulated differences of the corresponding data units of the two halveand stores the index value in memory 110. The index value is then used,either alone or in conjunction with other metrics of the data matrix ororiginal image data, to identify an APK. The index value may be comparedto a predetermined threshold and/or may be included in a more complexfunction of metrics of the matrix and/or original image. In thisembodiment, processor 108 determines the RMS of the differences betweenthe corresponding data units, which provides the index value. The indexvalue may be further normalized based on the mean value of the matrixdata units involved in the difference calculations so that thebrightness and/or darkness of the underlying image does not impact theindex value sufficiently to affect the conclusion of the index value.The RMS process has already removed the impact of the matrix size, butother accumulations of the differences between the corresponding dataunits may be considered suitable matrix size normalization steps. Inanother example, the index value may be provided from a dot-product ofeach of the differences of the corresponding data units. The dot productis accumulated and normalized by the number of data units relied on inthe dot product accumulation. One or more other methods may be used tocombine the differences of the corresponding units for comparison to apredetermined threshold, or thresholds, for determining either imagejudgment as APK, not APK, or potential APK, or for sample dispensationas PASS, FAIL, or REVIEW, if further, more detailed analysis may benecessary. In one or more embodiments, the predetermined threshold orthresholds are based on empirical data collected from multiple uses ofthe methods described herein on materials known to include APK andnon-APK anomalies. The predetermined thresholds can further be adjustedupward or downward to change the confidence in the identification ofAPKs. Typically multiple metrics of the matrix and/or original image areused to most clearly identify APK-like images from images that are notlikely to be APK. In some cases, other images at higher resolution maybe required, or other metrology techniques may be used, prior to thefinal classification of the anomalies. This method may be applied, insome embodiments, to lower resolution and higher resolution images ofthe anomalies.

Furthermore, in various embodiments, processor 108 calculates multipleindex values based on multiple axes of the matrix and store the multipleindex values in memory 110. In this embodiment shown in FIG. 5, fourindex values are determined for each pairs of halves or each pair ofmatrixes (flipped and original). Specifically, an index value iscalculated for the differences of corresponding data units for each ofthe left/right halves, top/bottom halves, the top-right/bottom-lefthalves and the top-left/bottom-right halves. In this manner, processor108 substantially limits the potential for misidentifying an APK for asubstantially symmetric shape, such as, for example, a cross or adiamond. It should be appreciated that multiple different axes orientedin a variety of different directions may be used to determine multipledifferent index values. The number and orientations of the axes aregenerally selected to provide at least a threshold level of confidencein detecting an APK, rather than a piece of debris or other non-APKanomalies within material 104.

When multiple index values are calculated, the identification of the APKis based on a combination of the multiple index values, as compared tothe predetermined value. In this embodiment, each of the index valuesfor the four axes are added together to provide a total index value forthe matrix. The mean and maximum index values are also potentiallyuseful metrics of the matrix. The predetermined threshold is provided toindicate whether the total index value is indicative of an APK or anon-APK anomaly in the material 104. Additional information may befurther included, prior to comparison to a predetermined threshold. Inone example, a ratio of the long radius to a short radius of an ellipseis added to the multiple index values. The ellipse may be based on thebinary blob image, such as a best-fit ellipse, for example using themethod of moments of the found anomaly image blob. Other values ormetrics indicative of the attributes of the image object or binary blobmay be further included to enhance the accuracy of detection of APK andnon-APK anomalies.

In this embodiment, after the one or more fold/difference orflip/difference operations, processor 108 may normalize the imagerepresentative of the differences (e.g., FIGS. 2C and 3C) to account forthe 2-dimensional size of the region of interest and/or the intensity ofthe region of interest. More specifically, in this embodiment,normalizing the image may include dividing the index value by a totalnumber of pixels and/or dividing the index value by average intensity. Avariety of different operations may be provided to normalize the imagedata and/or index value to provided accurate comparison to a singlepredetermined threshold. More generally, the data for each material 104is processed sufficiently so that the three dimensional (3D) shape ofthe image object controls the determination of whether the image objectis an air pocket, or something else. Intensity and size may contributeto the determination, but the 3D shape is the main characteristic undertest, through the operations described herein. Normalization of theimage data occurs after the folding or flipping operations in thisexemplary embodiment, but may be completed prior to one or more foldingoperations in other embodiments.

It should be appreciated that one or more aspects of the presentdisclosure transform a general-purpose computing device into aspecial-purpose computing device when configured to perform thefunctions, methods, and/or processes described herein.

Embodiments herein may be described in the general context ofcomputer-executable instructions, such as program components or modules,stored on one or more non-transitory computer-readable storage media andexecuted by one or more processors. Aspects described herein may beimplemented with any number and organization of components or modules.For example, aspects of the disclosure are not limited to the specificcomputer-executable instructions or the specific components or modulesillustrated in the figures and described herein. Alternative embodimentsof the invention may include different computer-executable instructionsor components having more or less functionality than illustrated anddescribed herein.

The order of execution or performance of the operations in theembodiments illustrated and described herein is not essential, unlessotherwise specified. That is, the operations may be performed in anyorder, unless otherwise specified, and embodiments of the invention mayinclude additional or fewer operations than those disclosed herein. Forexample, it is contemplated that executing or performing a particularoperation before, contemporaneously with, or after another operation iswithin the scope of aspects of the invention.

When introducing elements of the present disclosure or the embodimentsthereof, the articles “a”, “an”, “the” and “said” are intended to meanthat there are one or more of the elements. The terms “comprising,”“including,” “containing” and “having” are intended to be inclusive andmean that there may be additional elements other than the listedelements. The use of terms indicating a particular orientation (e.g.,“top”, “bottom”, “side”, etc.) is for convenience of description anddoes not require any particular orientation of the item described.

As various changes could be made in the above constructions and methodswithout departing from the scope of the disclosure, it is intended thatall matter contained in the above description and shown in theaccompanying drawings shall be interpreted as illustrative and not in alimiting sense.

What is claimed is:
 1. A computer-implemented method for use indetecting an air pocket in a single crystal material, said methodcomprising: providing a matrix including a plurality of data units and acenter, the plurality of data units including image data related to aregion of interest of the single crystal material; defining a first halfand a second half of the matrix based on a first axis passing throughthe center of the matrix; determining, by a processor, a differencebetween each data unit of the first half and a corresponding data unitof the second half; calculating, by the processor, a first index valuebased on the determined differences; defining a third half and a fourthhalf of the matrix based on a second axis passing through the center ofthe matrix, the second axis being different than the first axis;determining, by the processor, a difference between each data unit ofthe third half and a corresponding data unit of the fourth half;calculating, by the processor, a second index value based on thedifferences of the corresponding data units of the third and fourthhalves; and identifying an air pocket within the single crystal materialbased on the first index value, the second index value, and apredetermined threshold.
 2. The method of claim 1, further comprising:calculating, by the processor, a third index value based on differencesbetween corresponding data units of a fifth half and a sixth half, thefifth and sixth halves defined by a third axis different than the firstand second axes; and calculating, by the processor, a fourth index valuebased on differences between corresponding data units of a seventh halfand an eighth half, the seventh and eighth halves defined by a fourthaxis different than the first, second, and third axes; whereinidentifying an air pocket within the single crystal material is furtherbased on the third and fourth index values.
 3. The method of claim 2,wherein the first, second, third and fourth index values are aroot-mean-square of the differences of the corresponding data units. 4.The method of claim 1, further comprising identifying a blob of dataunits within an image of the single crystal material as the region ofinterest.
 5. The method of claim 1, further comprising at least one ofpadding the region of interest with additional data units to provide thematrix and clipping data units from the region of interest to providethe matrix.
 6. The method of claim 1, further comprising normalizing atleast one of the matrix and the first index value based on at least oneof the size of the matrix and an intensity associated with the region ofinterest.
 7. The method of claim 1, wherein providing the matrixincludes locating a square matrix on a portion of the image dataincluding the region of interest.
 8. The method of claim 7, whereinlocating the square matrix includes substantially centering the squarematrix on the region of interest.
 9. The method of claim 1, furthercomprising normalizing at least one of the matrix and the first indexvalue based at least in part on an intensity associated with the imagedata.
 10. The method of claim 1, further comprising normalizing at leastone of the matrix and the first index value based at least in part on asize of the region of interest.
 11. The method of claim 1, wherein thesingle crystal material includes one of a silicon material, a germaniummaterial, and a gallium arsenide material.
 12. A detection system foruse in detecting an air pocket within a material, said detection systemcomprising: a light source configured to emit near-infrared (NIR) lighttoward a material; a detection device positioned adjacent to thematerial to capture image data based on light passing through thematerial; and a processor coupled to said light source and saiddetection device, said processor configured to: provide a matrixincluding a plurality of data units and a center, the plurality of dataunits including image data related to a region of interest of the singlecrystal material; define a first half and a second half of the matrixbased on a first axis passing through the center of the matrix;determine a difference between each data unit of the first half and acorresponding data unit of the second half; calculate a first indexvalue based on the determined differences; define a third half and afourth half of the matrix based on a second axis passing through thecenter of the matrix, the second axis being different than the firstaxis; determine a difference between each data unit of the third halfand a corresponding data unit of the fourth half; calculate a secondindex value based on the differences of the corresponding data units ofthe third and fourth halve; and identify an air pocket within the singlecrystal material based on the first index value, the second index value,and a predetermined threshold.
 13. The detection system of claim 12,wherein said processor is further configured to normalize at least oneof the matrix and the first index value based on at least one of a sizeof the matrix and an intensity associated the region of interest. 14.The detection system of claim 12, wherein said processor is configuredto determine the root-mean-square of the differences of thecorresponding data units to calculate the first index value.
 15. Thedetection system of claim 12, wherein the plurality of data unitsinclude a plurality of pixels.
 16. The detection system of claim 12,wherein the single crystal material includes one of a silicon material,a germanium material, and a gallium arsenide material.
 17. The detectionsystem of claim 12, wherein said processor is configured tosubstantially center the matrix on the region of interest.
 18. One ormore non-transitory computer-readable storage media havingcomputer-executable instructions embodied thereon, wherein when executedby at least one processor, said computer-executable instructions causethe at least one processor to: identify a matrix including a pluralityof data units and a center, the plurality of data units including imagedata related to a region of interest of the single crystal material;define a first half and a second half of the matrix based on a firstaxis passing through the center of the matrix; determine a differencebetween each data unit of the first half and a corresponding data unitof the second half; calculate a first index value based on thedetermined differences; define a third half and a fourth half of thematrix based on a second axis passing through the center of the matrix,the second axis being different than the first axis; determine adifference between each data unit of the third half and a correspondingdata unit of the fourth half; calculate a second index value based onthe differences of the corresponding data units of the third and fourthhalves; and identify an air pocket within the single crystal materialbased on the first index value, the second index value, and apredetermined threshold.
 19. The non-transitory computer-readablestorage media of claim 18, wherein the computer-executable instructionfurther cause the processor to normalize at least one of the matrix andthe first index value based on at least one of a size of the matrix andan intensity associated the region of interest.
 20. The non-transitorycomputer-readable storage media of claim 18, wherein thecomputer-executable instruction further cause the processor to determinethe root-mean-square of the differences between the data units and thecorresponding data units in order to calculate the first index.